Dispersal potentials determine responses of woody plant species

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Forest Ecology and Management 255 (2008) 2894–2906
www.elsevier.com/locate/foreco
Dispersal potentials determine responses of woody plant species richness
to environmental factors in fragmented Mediterranean landscapes
Abelardo Aparicio a,*, Rafael G. Albaladejo a, Miguel Á. Olalla-Tárraga b,
Laura F. Carrillo a, Miguel Á. Rodrı́guez b
a
Departamento de Biologı́a Vegetal y Ecologı́a, Universidad de Sevilla, c/Prof. Garcı́a González n8 2, 41012 Sevilla, Spain
b
Departamento de Ecologı́a, Facultad de Biologı́a, Universidad de Alcalá, 28871 Alcalá de Henares, Madrid, Spain
Received 12 November 2007; received in revised form 18 January 2008; accepted 21 January 2008
Abstract
While maximizing plant species richness continues to be central in the design, conservation and reforestation action plans, plant life histories
are receiving increasing attention in assessments for the conservation of biodiversity in fragmented landscapes. We investigated the determinants of
woody plant species (trees, shrubs and climbers) richness in the forest patches of the Guadalquivir river valley, a Mediterranean agricultural
landscape with 1% forest cover. We analyzed three species richness variables, total, and those corresponding to species with short-distance
(ballistic, barochorous, myrmecochorous and short-distance anemochorous) and long-distance (anemochorous, endozochorous, exozoochorous,
hydrochorous and dyszoochorous) dispersal systems, which significantly characterize earlier and late successional stages, respectively. We
selected eleven predictor variables related to habitat structure (patch area, shape, distances to the nearest patch and reserve, and general isolation),
physical environment (temperature, precipitation, elevation, and lithological heterogeneity), and anthropogenic influences (disturbance and
proportion of old-growth forest). We used ordinary-least-squares multiple regression (OLS) and the Akaike’s information criterion (corrected for
spatial autocorrelation) and derived indices to generate parsimonious models including multiple predictors. These analyses indicated that plant
species richness increase primarily along with increasing patch area and decreasing disturbance, but also detected secondary effects of other factors
when dispersal was considered. While the number of species with potential long-distance dispersal tended to increase in more isolated patches of
areas with greater precipitation and lithological heterogeneity (e.g. highlands at the valley edges), the number of species with short-distance
dispersal increased towards drier and less lithologically complex zones with shorter between-patch distances (e.g. central lowlands). Beyond
emphasizing the need to consider dispersal in fragmentation studies, our results show that woody plant species richness would be favoured by
actions that increase patch area and reduce anthropogenic disturbances particularly in lowland forests.
# 2008 Elsevier B.V. All rights reserved.
Keywords: Biodiversity conservation; Habitat fragmentation; Mediterranean forest restoration; Seed dispersal; Woody plant species richness
1. Introduction
Deforestation begun in Europe 6.0 ka BP when Neolithic
agriculturalist settlements began to clear forests for cultivation,
grazing, and obtaining fodder (Williams, 2000). This process of
forest destruction and fragmentation has been particularly
intense and severe in the Mediterranean region (Valladares
et al., 2004), where forest fragments are frequently sparsely
distributed across an agricultural matrix of extensive cultiva-
* Corresponding author. Tel.: +34 95 4556787; fax: +34 95 4233765.
E-mail addresses: [email protected] (A. Aparicio), [email protected]
(R.G. Albaladejo), [email protected] (M.rraga), [email protected] (L.F. Carrillo),
[email protected] (M.guez).
0378-1127/$ – see front matter # 2008 Elsevier B.V. All rights reserved.
doi:10.1016/j.foreco.2008.01.065
tions. Still, this region is considered a hot spot for biological
diversity (Médail and Quézel, 1997), and although its relictual
forested landscape (sensu McIntyre and Hobbs, 1999) is far
from a pristine example of Mediterranean vegetation, it often
contains unique populations of endemic plant species (Garrido
et al., 2002; Aparicio, 2005).
It is important to understand the function of these landscapes
as plant diversity reservoirs and how their diversity relates to
characteristics of the remaining habitat fragments. Among
these, forest cover is considered the pre-eminent determinant of
forest species richness (Boutin and Hebert, 2002; Fahrig, 2003).
However, for the case of relict landscapes, where the amount of
forest cover drops to 10% or below (McIntyre and Hobbs,
1999), habitat structure-related attributes such as patch size,
shape and spatial configuration may also have strong impacts
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A. Aparicio et al. / Forest Ecology and Management 255 (2008) 2894–2906
on plant species richness (Saunders et al., 1991; Boutin and
Hebert, 2002). The main goal of this study is to document
relationships of woody species richness with habitat configuration in the forest patches of the Guadalquivir river
depression, a relict landscape of southwestern Spain where
natural or semi-natural forest retention is about 1% (Aparicio,
2008).
Because maximizing species richness has been central to
set targets for conservation and to design conservation and
reforestation action plans in general (Honnay et al., 1999;
Godefroid and Koedam, 2003; Peintinger et al., 2003; Desmet
and Cowling, 2004; Wilsey et al., 2005), species richness has
often been explored within the species–area relationship as
theoretical framework (Lomolino, 2000). However, the
generally highly stochastic processes of extinction and
recolonization determining within patch species richness do
not only depend on patch area, but also on characteristics of
the physical environment, disturbance regimes, and, notably,
plant life histories (Honnay et al., 1999; Butaye et al., 2001).
Consequently, the study of life history traits of plant species is
receiving increasing attention in assessments for the
conservation of biological diversity in fragmented landscapes
(Graae and Sunde, 2000; Benı́tez-Malvido and Martı́nezRamos, 2003; Kolb and Diekmann, 2005; Purves and
Dushoff, 2005; Wiegand et al., 2005; Chust et al., 2006;
but see Yates and Ladd, 2005). In particular, seed dispersal is
considered a functional core trait with relevance for both
understanding and predicting ecological patterns and processes associated with population dynamics and evolution
(Herrera, 1992; Weiher et al., 1999; Duminil et al., 2007), not
only at the species level but also for species assemblages
(Jacquemyn et al., 2001). In this regard, the spatial scale at
which dispersal operates is fundamental in biological
conservation since short-distance dispersal is primarily
related to local population recruitment, whereas long-distance
dispersal is much more influential on the potential of
colonization of new habitats, the migration capacity of the
species and the spatial genetic structuring of populations
(Calviño-Cancela et al., 2006).
According to Ozinga et al. (2005), the critical question for
conservation is not whether dispersal is an important process,
but whether differences in dispersal translate into differences in
local plant diversity. The response of each plant species to
habitat fragmentation may depend largely on its potential for
long-distance dispersal, relying on the type of diaspore (Butaye
et al., 2001; Ozinga et al., 2005; Chust et al., 2006). In principle,
from a functional perspective, morphological adaptations of
diaspores for animal- and wind-mediated dispersal (fleshy
pulps, wings, hooks, hair tufts) provide longer dispersal
distances compared to diaspores lacking such morphological
adaptations, or bearing food bodies for ant dispersal (Willson,
1993; Hughes et al., 1994). Thus, in theory, those species in
which long-distance dispersal can be facilitated by the
intervention of animals or wind should be less sensitive to
habitat fragmentation than species lacking this possibility, or
that are disseminated by animals with reduced home ranges
such as ants.
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Along with dispersal, responses of plant species richness to
both environmental variation and anthropogenic modifications
of habitat characteristics may be conditioned by a number of
interrelated life history characteristics including flowering
time, longevity, vegetative spread or life form (e.g. Tilman
et al., 1994; Kolb and Diekmann, 2005), but reliable
information to assign accurately these other traits to large
plant species sets rarely exists (Herrera, 1984, 1992). Yet
Herrera (1992) compiled up to ten of such morphological and
functional characters for up to 66 of the woody plant genera that
inhabit in the Mediterranean southwestern Spain. This allowed
him to classify the woody vegetation of this region into two
well-defined groups, each characterized by genera exhibiting a
particular association of traits or character syndrome (Herrera,
1992; see also Verdú et al., 2003). Among other traits, the first
syndrome corresponds to non-sclerophyllous, insect-pollinated, small-seeded dry-fruited lineages evolved during the
Quaternary (e.g. Cistus, Halimium, Thymus, Lavandula, Erica
or Calluna) and the second syndrome involves sclerophyllous,
wind-pollinated, large-seeded fleshy-fruited lineages already
evolved during the Tertiary (e.g. Pistacia, Osyris, Juniperus,
Rhamnus, Quercus or Myrtus). On the other hand, Verdú
(2000), Pausas and Verdú (2005) and Paula and Pausas (2006)
have shown that post-disturbance resprouting capacity (as
opposed to diaspore germination) is another common functional trait among the genera representative of the Tertiary set
of species and that the Quaternary species are more droughttolerant and fire-adapted. In all, while Tertiary species are
characteristic of pre-forestal, more successionally mature
communities, Quaternary species characterize earlier successional stages or woody pioneer plants (Herrera, 1992).
We asked three questions in this study. First, to what extent
characteristics of habitat configuration, physical environment
and anthropogenic influence determine total woody species
richness variation across the studied forest patches? Second, do
major plant dispersal potentials condition the response of
species richness to these factors? Third, what are the
implications of the observed relationships for the conservation
of woody species richness in Mediterranean relict landscapes?
To address these questions we have used data from the
‘Island-Forests of Western Andalusia’ database BIANDOCC
(property of the Andalusian Regional Government) generated
from a complete sampling that involved all the woody species
and all the forest patches currently occurring in the
Guadalquivir river valley (Aparicio, 2008).
2. Methods
2.1. Study area
The study area in the project BIANDOCC extends from the
Atlantic coast through the mean and lower stretches of the
Guadalquivir river valley, a landscape of about 21,100 km2
dominated by a fairly uniform agricultural matrix (Fig. 1). The
climate is mild Mediterranean, with cool humid winters and
warm, dry summers. Annual precipitation ranges from 460 to
1027 mm and mean annual temperature from 15.1 to 18.5 8C.
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Fig. 1. Studied area in Western Andalusia (outlined). Light colour is the Guadalquivir river valley (<200 m in altitude) where the studied forest patches are
embedded. For this study, forest stands having high three (>50%) and shrub (>25%) cover and at least four native woody species were selected (N = 237). National
Parks and Natural Parks in Western Andalusia are shaded and labelled.
The dominant orography consists in low plains intermixed with
small hills and the elevation ranges from sea level to 200 m.
Lithologically, the area is made up of sediments from the
Miocene to the Quaternary, and its soils are derived from marls,
clays, limestones, chalkstone calcareous and siliceous sandstones, and silty alluvials from the Guadalquivir river and its
tributaries (Jordán, 1999). The dominant tree species within the
area are Quercus ilex subps. ballota, Q. suber, Pinus pinea, P.
pinaster and P. halepensis, while the dominant cultivations
consist of olives, cereals, sunflower, beetroot, cotton, legumes,
opium poppies, vineyards, orchards, and greenhouses, along
with oranges, figs, pine trees, and Eucalyptus plantations (a
detailed description of BIANDOCC project can be found in
Aparicio, 2008).
2.2. Forest patches and botanic sampling
We focused our analyses on 237 forest patches embedded in
the agricultural matrix of the Guadalquivir river depression
covering in all 0.64% of the area (i.e. 135.2 km2). These units
represent all woody vegetation patches showing clear natural or
semi-natural forest characteristics with tree cover 50%, a
shrub cover 25%, and, at least, four native woody species. We
excluded all exotic tree plantations, as well as all other highly
managed woody vegetated areas that were too open and
impoverished in tree/shrub species as to be considered as
forests (e.g. patches of ‘dehesa’ which are savannah-like
formations typical of Spanish extensive farmlands). Following
sampling procedures described in Gotelli and Colwell (2001),
the botanic sampling of the forest patches was done between
1998 and 2001 and involved two to three researchers randomly
walking the patches until identifying all woody species. For
each patch, the time devoted to sampling was proportional to its
area and visual heterogeneity (cf. Kirby et al., 1986).
2.3. Dispersal abilities and richness variables
We generated three measures of woody plant species
richness for each patch: (1) total species richness; (2) shortdistance dispersal species richness, and (3) long-distance
dispersal species richness. We categorized each species as
having either short-distance dispersal (S) (including ballistic,
barochorous, myrmecochorous and short-distance anemochorous) or potential for long-distance dispersal (L) (including
anemochorous, endozochorous, exozoochorous, hydrochorous
and dyszoochorous) by surveying the primary botanical
literature (including the Seed Information Database, Flynn
et al., 2006) for specific information on dispersal modes, either
referred to particular species or to congenerics (see
Appendix A). We obtained information for 86% of the species,
while for the rest the dispersal mode was assigned assuming a
standard dispersal system (Higgins et al., 2003) and taking into
account diaspore morphologies as described in the regional
flora (Valdés et al., 1987). Given the low number of candidate
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species (see Appendix A) and our broad interest in detecting
patterns of species richness at the regional scale, we have ruled
out the influence of non-standard long-distance and secondary
dispersal events (Higgins et al., 2003).
In addition, we have also investigated to what extent our
assignment of woody species to these two dispersal categories
could be used as indicative of the broader complexes of life
history traits analyzed by Herrera (1992). For this, we focused
on the 50 genera common to both studies and performed a
genera-level analysis. Firstly, we ranked these genera from 1 to
3 according to their dispersal potentials, with 1 indicating longdistance dispersal (26 genera), 3 short-distance dispersal (21
genera), and 2 the genera with either type of dispersal (3
genera). Then we related this ordinal (rank order) variable with
Herrera’s (1992) ‘Dimension 1’, a composite, continuous
variable that corresponds to the first dimension of an ordination
plane generated by this author using Nonmetric Multidimensional Scaling (NMDS). According to Herrera (1992), this
variable provides a coherent synthesis of the across-genera
similarities of 10 functional and morphological traits – referred
to habit (deciduousness, spinescence, and sclerophylly), flower
biology (flower size and sexuality, perianth colour and degree
of reduction, and pollinating agent) and seed dispersal (seed
size and dispersal agent) – (for details see Herrera, 1992).
2.4. Predictor variables
We generated eleven potential predictor variables indicative
of major characteristics of (i) habitat structure, (ii) the physical
environment, and (iii) current and past anthropogenic
influences (see Jacquemyn et al., 2003; Maestre, 2004).
2.4.1. Habitat structure
Fragmentation is a process causing the division of the
original habitat into a constellation of habitat remnants
differing in size, shape, and connectivity (Franklin et al.,
2002; Fahrig, 2003). Accordingly, we used (1) patch area (in
hectares), (2) the Patton’s I shape index (Patton, 1975)
computed as:
I¼
P
pffiffiffiffiffiffi
200 pA
where P is patch perimeter in meters, and A its area in hectares),
(3) the Euclidean distance (edge-to-edge) to the nearest neighbouring patch and to the nearest reserve, and (4) the general
proximity index (Gustafson and Parker, 1992), taking into
account the combined effects of the amount and spatial configuration of the forested area existing in the vicinity of each
patch. This index is computed as:
proximity ¼
n
X
Aik
k¼1
dik2
where Aik is the area (in hectares) of the patch i within a userspecified neighbourhood radius, and dik is the distance (in meters)
between the focal patch and the patch i. The greater the occupancy of the neighbourhood by forest patches and, especially, the
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shorter the distances of these patches to the focal patch, the higher
the value of the index. To optimize this variable, we assayed six
neighbourhood radiuses from 250 to 10,000 m seeking the highest correlation between proximity and the response variables.
Finally, we used the proximity index at 5000 m.
We obtained all these variables either directly from digital
coverages superimposed on digitized aerial orthophotos taken
in 2002 (available at http://desdeelcielo.andaluciajunta.es) or
from a 30 m cell grid generated with ArcGis 9.0. Euclidean
distance and the proximity index were computed in FRAGSTATS 3.3 (McGarigal et al., 2002).
2.4.2. Physical environment
Environmental heterogeneity is responsible for species
richness at landscape scales (Pausas et al., 2003). So, we used
mean annual temperature and annual precipitation to capture
major gradients of warmth and humidity across the landscape.
These data were taken at the patch centroids from 1-km
resolution rasters generated by the Instituto Nacional de
Meoteorologı́a (www.inm.es) using interpolated meteorological station records for the period 1971–2000. We also obtained
the patch-centroid elevation to use it as a surrogate for patch
microclimate characteristics not taken into account by the other
variables (data taken from: www.juntadeandalucia.es/medioambiente). Finally, we assessed lithological heterogeneity at
patch level by counting the number of lithological units existing
within each patch (lithological data from Jordán, 1999).
2.4.3. Anthropogenic influence
Current human disturbance level was visually assessed in the
field with respect to a range of activities (including forest
management practices, public use, cattle grazing and/or
trampling, fires, hunting, tracks, and buildings). This allowed
us to generate a semi-quantitative variable in which each patch is
assigned with a value between 1 and 4, where 1 represents
minimum human pressure. In addition, from a short-term
historical perspective, we found, comparing two sets of digitized
aerial orthophotos dating from 1956 and 2002 (a 46 years time
span), that forest patches have either decreased or increased their
total area. Accordingly, we generated a variable that takes into
account the proportion of old-growth forest area relative to the
total area for each patch by clipping the two digital coverages (i.e.
forest patches from 1956 to 2002) in ArcGis 9.0, and classifying
as old-growth forest the corresponding overlapping surfaces.
2.5. Data analysis
Gamma correlation is the rank order index to be used when
the data contain an elevated number of tied observations
(StatSoft, 2001) – as it is the case of the variable ‘dispersal
potential’ –, so we used this index to check the degree of
association between our assigned dispersal categories and
Herrera’s (1992) ‘‘Dimension 1’’ (see above).We generated
models including multiple predictors for each species richness
variable by using ordinary-least-squares (OLS) multiple
regression, which is known to perform poorly when using
highly collinear variables. To overcome this, we first calculated
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the Pearson’s correlation for all possible pairs of predictors and
excluded those showing high correlation with at least another
predictor (r > j0.5j; Booth et al., 1994). Additionally, we used a
model selection procedure based on Information Theory which
deals efficiently with collinearity among predictor variables
(see Burnham and Anderson, 2002; Johnson and Omland,
2004). This procedure is based on generating information
indexes of relative support for all possible models (i.e. for all
possible combinations of predictors). We utilized the Akaike’s
information criterion (AIC) complemented with the DAIC index
(i.e. the difference between the AIC of each model and the
minimum AIC found) to identify which models have substantial
support (i.e. DAIC 2) and fit almost equally well as the best
model (Burnham and Anderson, 2002). Also, we used the 25
models with lowest AICs to calculate their Akaike’s weighting
(wi ), an index that can be interpreted as the probability that the
model i is actually the best explanatory model.
Additionally, we took into account that our species richness
and environmental data may display spatial autocorrelation due
to the varying proximities of the analysis units (i.e. forest
patches) in the studied landscape. This spatial dependence of
the analysis units may generate bias in common statistical
procedures due to overestimation of the sample size.
Accordingly, we used the modified t-test of Dutilleul (1993)
to obtain spatially unbiased significance estimates of Pearson’s
correlations (e.g. Legendre et al., 2002). We acknowledge that
this is a highly conservative correction that we used as a way to
identify particularly strong relationships and thus give more
focus to our interpretations (JAF Diniz-Filho pers. comm.).
Also we computed all AICs and derived indexes using corrected
variances for the presence of spatial autocorrelation in the
residuals of the regression models (for a detailed description of
this method see Olalla-Tárraga et al., 2006; Olalla-Tárraga and
Rodrı́guez, 2007). Finally, we investigated the effectiveness of
our models to account for spatially structured patterns in the
data by generating Moran’s I spatial correlograms for both the
three response richness variables, and the residuals resulting
after fitting the models (Diniz-Filho et al., 2003).
We applied log or angular transformation to all variables as
appropriate before analysis (Tabachnick and Fidell, 1996), and
performed all statistical analyses in STATISTICA 6.0 (StatSoft,
2001) and SAM 1.1 (Spatial Analysis in Macroecology; Rangel
et al., 2006).
3. Results
Mean (SD) area of the forest patches was 55 (100)
hectares (range 0.3–752), and the median was 2.27 ha
(N = 237). Mean Euclidean distance (edge-to-edge) among
patches was 827 (1570) m (range 60–20 701). The number of
woody plant species was 143 with a mean (S.D.) of 18.96
(7.7) species per patch. The sets of short- and long-distance
dispersal species were comprised by 83 (58%) and 60 (42%)
species (see Appendix A), with mean (SD) patch values of
9.05 (4.72) and 9.87 (4.83) species, respectively. The
association between our ‘dispersal potential’ variable and
Herrera’s (1992) ‘Dimension 1’ (see Methods), was very high
(Gamma r = 0.806, P 0.001), thus supporting that our
assignation of broad dispersal characteristics is to a large extent
capturing the characteristic trait syndromes described by this
author for the genera in the flora of south-western Spain.
On the other hand, after correcting probability levels for
spatial autocorrelation, all species richness variables were
significantly positively correlated with patch area and
negatively with current disturbance, and total and long-distance
species richness were also significantly positively correlated
with patch shape and lithological heterogeneity (Table 1). Due
to collinearity among predictor variables and weak relationships with richness (Table 2), patch shape, proximity and
elevation were excluded from the analysis, and thus we used a
final set of eight variables for multiple regression modelling.
3.1. Total species richness
Out of the 255 possible multiple-regression models for total
species richness, 13 models had a DAIC 2 and accounted for
Table 1
Pearson’s product moment correlations between response (total species richness, short- and long-distance dispersal species richness) and predictor (habitat structure,
physical environment, anthropogenic influence) variables used for model construction
Total species
Short-distance dispersal species
Long-distance dispersal species
Habitat structure
Area a
Shape
Distance to nearest reserve
Distance to nearest neighbour
Proximity
0.454***
0.312***
0.088
0.036
0.098
0.199**
0.107
0.177
0.159
0.042
0.486***
0.350***
0.282
0.107
0.077
Physical environment
Temperature
Precipitation
Elevation
Lithological heterogeneity
0.081
0.026
0.026
0.193***
0.096
0.337
0.137
0.130
0.225
0.340
0.154
0.413*
Anthropogenic influence
Current disturbance
Proportion of old-growth forest
0.367***
0.047
0.371***
0.049
0.205*
0.108
a
Significance levels have been corrected for spatial autocorrelation by the t-test of Dutilleul (1993). *P < 0.05, **P < 0.01, ***P < 0.001.
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Table 2
Pearson’s correlation matrix among predictor variables
Area
Habitat structure
Areaa
Shape
Distance to nearest
reserve
Distance to nearest
neighbour
Proximity
Physical environment
Temperature
Precipitation
Elevation
Lithological
heterogeneity
Shape
Distance Distance
to nearest to nearest
reserve
neighbour
Proximity
Temperature Precipitation Elevation Lithological Current
heterogeneity disturbance
0.686***
0.262
0.245
0.064
0.136
0.036
0.417*
0.223**
0.295**
0.094
0.22
0.061
0.426***
0.003
0.311
0.254
0.652
0.14
0.426
0.437*** 0.242
Anthropogenic influence
Current disturbance
0.122
Proportion old-growth
0.238**
forest
0.001
0.166*
0.082
0.242
0.524***
0.107
0.035
0.007
0.138
0.139
0.402*** 0.287
0.262*
0.718***
0.072
0.188
0.367*
0.435
0.043
0.164
0.049
0.109
0.115
0.139
0.166
0.256
0.169
0.109
0.179
0.019
0.145
0.002
*P < 0.05, **P < 0.01, ***P < 0.001.
a
Significance levels have been corrected for spatial autocorrelation by the t-test of Dutilleul (1993).
similar proportions of variance (ca. 31%) (Table 3). Only two
variables entered in all the models: patch area with positive sign
and current disturbance with negative sign. Other variables
entered in some models, but were of secondary importance
according to their low standardized regression coefficients (see
Table 3). The Moran’s I correlogram for total species richness
showed no evidence of spatial autocorrelation across all
distance classes (Fig. 2a).
3.2. Short-distance dispersal species richness
On the basis of a DAIC 2, six models were equivalent in
describing short-distance species richness (Table 3), and
accounted for similar proportions of variance (ca. 31%).
However, based on Akaike’s weightings, none of the models
received an overwhelming support, with the best four models
having wi values from 0.20 to 0.06. Five variables entered in all
six models, which were related to habitat features (patch area
and distance to the nearest neighbour), physical environment
(precipitation), and anthropogenic influence (current disturbance and proportion of old-growth forest). Based on the
standardized regression coefficients, precipitation and current
disturbance were the strongest predictors of short-distance
dispersal especies richness, both with negative sign. Distance to
the nearest neighbour and, notably, patch area showed discrete
negative and positive associations with this richness variable,
Table 3
Standardized regression coefficients of the variables and coefficients of determination (r2) of the multiple regression models obtained for total, short- and longdistance dispersal species richness
Model
Habitat structure
Area
Physical environment
Distance
to nearest
reserve
Distance
to nearest
neighbour
Temperature
Precipitation
0.014
0.001
0.030
0.008
Short-distance dispersal richness
Best
0.247
Average (6 models)
0.255 0.005
0.160
0.153
0.012
Long-distance dispersal richness
Best
0.394
Average (4 models)
0.395 0.059
0.101
0.104
0.141
0.155
Total richness
Best
Average (13 models)
0.429
0.429
Anthropogenic influence
Lithological
heterogeneity
r2
AIC a
wi b
Current
disturbance
Proportion
of old-growth
forest
0.317
0.310
0.053
0.025
0.308
0.309
123.250
121.823
0.122
0.003
0.338
0.319
0.301
0.305
0.095
0.098
0.305
0.307
82.105
81.086
0.203
0.030
0.169
0.186
0.138
0.132
0.212
0.174
0.381
0.383
171.121
169.877
0.327
0.049
For brevity, we report only two models for each species group: the best model (the one with the lowest AIC) and an average model obtained from averaging all models
with (AIC 2 (see Section 2). The number of models averaged in each case is indicated in parentheses.
a
Values of Akaike Information Criterion (AIC) computed with corrected variances for the presence of spatial autocorrelation in the residuals.
b
Akaike’s weightings (wi ) calculated over the 25 models with the lowest AICs.
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A. Aparicio et al. / Forest Ecology and Management 255 (2008) 2894–2906
which exhibited opposite signs in each case (Table 3). This
indeed suggests that each species richness variable is
responding differently to environmental variation. On the
other hand, the pattern of spatial autocorrelation for longdistance dispersal species richness was again representative of a
cline (Fig. 2c), and the model with the highest wi accounted for
virtually all of this pattern.
4. Discussion
4.1. Determinants of richness
Fig. 2. Spatial correlograms using Moran’s I for woody species richness (solid
circles) and residuals of the best-fitted multiple models (open circles). All
spatial correlograms were computed for 16 distance (in km) classes.
respectively. The amount of old-growth forest also entered in all
the selected models displaying a slight negative effect. The
pattern of spatial autocorrelation for short-distance dispersal
species richness was characteristic of a cline with positive
autocorrelation at shorter distances and gradually becoming
negative at larger distances, except for the largest one, which
showed negligible autocorrelation (Fig. 2b). The best multiple
regression model removed most of this pattern, indicating that
our environmental models are sufficient to account for the
spatially structured variation of short-distance dispersal species
richness in the studied landscape.
3.3. Long-distance dispersal species richness
Finally, four models were equivalent in explaining longdistance species richness variation (Table 3), all of them
accounting for similar proportions of variance (ca. 38%). Six
predictor variables (namely, patch area, distance to the nearest
neighbour, temperature, precipitation, lithological heterogeneity, and current disturbance) were present in all the models, of
which patch area exhibited the highest standardized regression
coefficient. Notably, although patch area and current disturbance show the same signs (positive and negative,
respectively) as those obtained for short-distance dispersal
species richness, this was not the case for the other predictors,
We have investigated the influence of habitat structure,
physical environment and anthropogenic disturbance on woody
plant species richness across the forest patches that exist
sparsely in the agricultural matrix of the Guadalquivir river
valley, a relictual Mediterranean landscape intensively managed since pre-Roman times. Because dispersal capabilities
might determine species responses to habitat fragmentation, we
explored three richness variables, one including all species, and
the other two involving species with either short- or longdistance seed dispersal system. In all three cases, species
richness decreased with intra-patch disturbance levels, and
increased along with increasing forest patch area, the latter
being a result commonly reported in the literature on habitat
fragmentation (reviewed by Fahrig, 2003).
We also found weak associations between total woody
species richness and the rest of the predictors, which could be
interpreted as that neither fragmentation nor major aspects of
the physical environment are relevant for plant richness
variation. However, the patterns exhibited by short-distance
versus long-distance dispersal species richness could lead to a
different interpretation. First, with regard to fragmentation, the
regression models suggested that distance to the nearest
neighbouring patch had detectable effects in determining the
spatial variation of both species richness variables, albeit these
effects were not as strong as those of habitat area and
disturbance (Table 3). In addition, the regression models also
indicated that each group of species was differently associated
with this distance, so that an increasing separation between
forest patches led to the increase of species with good dispersal
abilities and to the diminution of species with limited dispersal.
Bearing in mind that both plant groups show fairly high species
numbers in the study area (83 and 60 species, respectively),
these contrasting trends are likely to have counterbalanced each
other when all species were pooled together to attain total
richness values. In other words, considering that dispersal
limits species responses to habitat fragmentation (Jacquemyn
et al., 2001; Purves and Dushoff, 2005), the lack of relationships we observed between total richness and fragmentation
variables might be accounted for by the occurrence of woody
species with different dispersal potentials.
Second, with regard to the effects of other aspects of the
environment on species richness, these were also highlighted
once dispersal abilities were taken into account. Thus, the
regression models detected clearer effects of temperature,
lithological heterogeneity, proportion of old-growth forest and,
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A. Aparicio et al. / Forest Ecology and Management 255 (2008) 2894–2906
specially, precipitation when species richness was separately
analyzed for short-distance versus long-distance dispersal
species, and, interestingly, that the effects of each of these
variables on each plant group were opposite in sign (Table 3).
On the one hand, such contrasted responses are, again, likely to
have counterbalanced each other when both plant groups were
summed up to obtain total species richness values, thus
explaining why total species richness showed so low
association with these environmental variables. On the other
hand, some of the relationships of short- and long-distance
dispersal species richness with these variables are easily
interpreted as direct responses of vegetation to its surrounding
environment (Pausas et al., 2003), but some others do not. For
example, the preference of plants for more mesic areas under
semiarid climates (Pugnaire and Lázaro, 2000) agrees with the
increase of long-distance dispersal species towards higher
precipitation areas, but contrasts with the negative association
between short-distance dispersal species richness and precipitation. Such difference is difficult to understand unless it
reflects indirect effects via competition between these two trait
syndrome cohorts at patch level; that is, an increase in the
number of long-distance dispersal species with precipitation
causes a reduction in the number of short-distance dispersal
species, which are known to be more drought-tolerant (Paula
and Pausas, 2006). Similar arguments could be used to interpret
the rest of the contrasted trends exhibited by both plant cohorts,
but this would be highly speculative given the non-experimental nature of our investigation. Consequently, we prefer to
emphasize that our data clearly put forward that the potential
for long-distance dispersal appears to determine, either directly
or indirectly, the responses of plant richness to many factors,
including characteristics of both habitat structure and physical
environment.
2901
models can be used as indicative of the sort of actions that might
push ecological succession forward across the forest patches of
the study area.
Specifically, our models suggest that a clear starting point to
enhance within-patch woody species richness is to increase
patch area while minimizing anthropogenic disturbance,
particularly because we have identified these two factors as
the ones most strongly associated with total, short- and longdistance dispersal species richness (Table 3). Beyond this, our
models also inform about where, in environmental terms,
controlling these factors is likely to have stronger effects on
richness and succession. For example, model regression
coefficients indicate that increasing patch area and reducing
disturbance would be more effective in promoting longdistance dispersal species richness – and, hence, pre-forestal
communities – in wet, cool areas with greater lithological
heterogeneity (e.g. at higher elevations in the edges of the
Guadalquivir river valley). Conversely, in lowlands, similar
effects on long-distance dispersal species richness and
succession would require larger increments of patch area
and reductions of disturbances, as these areas typically have
more homogeneous substrates, and warmer and drier climate.
To conclude, the most salient finding of our study is that
woody plant species richness not only responds to variation in
habitat area and human disturbance, as it is commonly reported
in the fragmentation literature, but also, and secondarily, to
habitat structure (nearest neighbour distance), climate and
characteristics of the substrate. However, these secondary
effects were only evident after discriminating between major
plant dispersal potentials, which emphasizes the need to
consider dispersal if we are to understand the determinants of
plant species richness in highly fragmented landscapes.
Acknowledgements
4.2. Maximizing species richness and successional rates
Our multiple regression models were built to be explanatory
(i.e. to identify key factors for the spatial variation of richness in
the studied landscape), not as predictive models to be used for
evaluating alternative scenarios in real conservation/restoration
projects. For this, a predictive approach should be adopted (e.g.
including higher order and interaction terms in the regressions),
thus making it possible to generate models that are less limited
than ours in accounting for richness variance (see Table 3) and,
hence, that could generate more accurate richness predictions.
Even so, we have statistically shown that the two dispersal
categories in our study actually synthesize broader complexes
of life history traits (Herrera, 1992; Verdú et al., 2003), which
allows discussing our model results in the context of ecological
succession. Thus, while short-distance dispersal is common
among non-sclerophyllous, insect-pollinated, dry-fruited, seeder species, typical of pioneer woody Mediterranean communities (e.g. Cistus, Halimium, Thymus, Lavandula, Erica,
Calluna), long-distance dispersal often occurs in species that
exhibit complementary character states, and that are typical of
mature communities (e.g. Pistacia, Osyris, Juniperus, Rhamnus, Myrtus) (Herrera, 1984, 1992). Therefore our regression
We are indebted to Teodoro Marañón, Carlos M. Herrera,
Fernando Valladares, Brad Hawkins and an anonymous
reviewer for constructive comments on this manuscript, and
to the EVOCA research group (http:/www.grupo.us.es/
grnm210) for helpful discussion of ideas. The project
BIANDOCC was originally launched and supported by the
Consejerı́a de Medio Ambiente (Andalusian Regional Government). The present study was supported by the Fundación
Banco Bilbao Vizcaya Argentaria (FBBVA grant to A. A.), by
the Spanish Ministry of Education and Science (grants
CGL2004-00022 to A. A; and REN2003-03989/GLO and
CGL2006-03000/BOS to M.Á.R.; and FPU fellowship
AP2005-0636 to M.Á.O.-T.) and the Consejerı́a de Innovación
Ciencia y Empresa (Andalusian Regional Government grant
P06-RNM-01499 to A.A.). The Spanish Ministry of Defence
provided permission to work with the 1956 digitalized aerial
orthophotos.
Appendix A
We show in this section the complete list of the 143
autochthonous (including Pinus spp.) woody and climber plant
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A. Aparicio et al. / Forest Ecology and Management 255 (2008) 2894–2906
species used in this study, the number of patches where they were
collected (N), and the assigned dispersal category accounting for
their long-distance dispersal ability. For 86% of the taxa,
dispersal mode was obtained from the literature, including the
Seed Information Database (Flynn et al., 2006). For the
remaining taxa (14%) for which apparently no bibliographic
data were available (empty cells in the last two columns), the
dispersal category was assigned assuming a standard-dispersal
Adenocarpus gibbsianus Castroviejo & Talavera
Adenocarpus telonensis (Loisel.) DC.
Anagyris foetida L.
Anthyllis cytisoides L.
Arbutus unedo L.
Aristolochia baetica L.
Asparagus acutifolius L.
Asparagus albus L.
Asparagus aphyllus L.
Bryonia dioica Jacq.
Bupleurum gibraltaricum Lam.
Calicotome villosa (Poiret) Link
Calluna vulgaris (L.) Hull
Ceratonia siliqua L.
Chamaerops humilis L.
Cistus albidus L.
Cistus crispus L.
Cistus ladanifer L.
Cistus libanotis L.
Cistus monspeliensis L.
Cistus populifolius L.
Cistus psilosepalus Sweet
Cistus salvifolius L.
Clematis cirrhosa L.
Clematis flammula L.
Colutea hispanica Talavera & Arista
Coronilla juncea L.
Crataegus monogyna Jacq.
Cytisus arboreus (Desf.) DC.
Cytisus grandiflorus DC.
Cytisus scoparius (L.) Link
Cytisus striatus (Hill) Rothm.
Daphne gnidium L.
Dorycnium hirsutum (L.) Ser.
Dorycnium pentaphyllum Scop.
Dorycnium rectum (L.) Ser.
Erica andevalensis Cabezudo & Rivera
Erica arborea L.
Erica australis L.
Erica ciliaris L.
Erica lusitanica Rudolphi
Erica scoparia L.
Erica umbellata L.
Ficus carica L.
Flueggea tinctoria (L.) G. L. Webster
Fraxinus angustifolia Vahl
Fumana ericifolia Wallr.
Fumana juniperina (Lag ex Dunal) Pau
Fumana thymifolia (L.) Spach ex Webb
Genista ancistrocarpa Spach
Genista cinerea (Vill.) DC.
Genista hirsuta Vahl
Genista polyanthos R. de Roemer ex Willk.
Genista triacanthos Brot.
Genista tridens (Cav.) DC.
N
Assigned
dispersal category
1
30
11
3
15
84
155
55
70
27
1
20
37
11
61
43
137
71
36
42
3
8
175
37
16
1
8
62
21
59
1
1
158
1
8
4
1
8
6
1
1
46
30
8
2
2
2
2
22
2
2
49
1
62
4
S
S
S
S
L
S
L
L
L
L
S
S
S
L
L
S
S
S
S
S
S
S
S
L
L
L
S
L
S
S
S
S
L
S
S
S
S
S
S
S
S
S
S
L
L
L
S
S
S
S
S
S
S
S
S
system (Higgins et al., 2003) based in the diaspore morphology
described in the regional flora (Valdés et al., 1987). Thus, the
long-distance dispersal species category (L, N = 60) includes
anemochorous (partly), endozochorous, exozoochorous, hydrochorous and dyszoochorous species, and the short-distance
dispersal species category (S, N = 83) includes ballistic,
barochorous, myrmecochorous and short-distance anemochorous species.
Type of dispersal
Reference
Wind, local dispersal
Animal
Ants
Animala
Animala
Animala
Animal
Pugnaire et al. (2006)
Flynn et al. (2006)
Berjano (2006)
Flynn et al. (2006)
Flynn et al. (2006)
Flynn et al. (2006)
Flynn et al. (2006)
Winda, local dispersala
Animal
Bullock and Moy (2004)
Flynn et al. (2006)
Unassisteda
Unassisteda
Unassisted
Unassitsed
Unassisteda,b
Unassisteda
Unassisteda
Unassisted, Ants
Winda
Wind
Wind
Flynn et al. (2006)
Flynn et al. (2006)
Flynn et al. (2006)
Flynn et al. (2006)
Flynn et al. (2006)
Flynn et al. (2006)
Flynn et al. (2006)
Troumbis and Trabaud (1986)
Flynn et al. (2006)
Flynn et al. (2006)
Flynn et al. (2006)
Animal
Auto, antsa
Auto, antsa
Auto, antsc
Auto, antsa
Animal
Short-distancea
Short-distancea
Short-distancea
Winda, local dispersala
Winda local dispersala
Winda local dispersala
Winda local dispersala
Winda local dispersala
Winda local dispersala
Winda local dispersala
Animal
Animala
Winda
Short-distancea
Short-distancea
Short-distancea,b
Antsa
Antsa
Antsa
Antsa
Antsa
Antsa
Flynn et al. (2006)
Flynn et al. (2006)
Flynn et al. (2006)
Malo (2004)
Flynn et al. (2006)
Flynn et al. (2006)
Bouza et al. (2002)
Bouza et al. (2002)
Bouza et al. (2002)
Bullock and Moy (2004)
Bullock and Moy (2004)
Bullock and Moy (2004)
Bullock and Moy (2004)
Bullock and Moy (2004)
Bullock and Moy (2004)
Bullock and Moy (2004)
Flynn et al. (2006)
Flynn et al. (2006)
Flynn et al. (2006)
Thanos et al. (1992)
Thanos et al. (1992)
Thanos et al. (1992)
Gómez and Oliveras (2003)
Gómez and Oliveras (2003)
Gómez and Oliveras (2003)
Gómez and Oliveras (2003)
Gómez and Oliveras (2003)
Gómez and Oliveras (2003)
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A. Aparicio et al. / Forest Ecology and Management 255 (2008) 2894–2906
2903
Appendix A (Continued )
N
Assigned
dispersal category
Type of dispersal
Reference
Genista umbellata (L’Hér.) Poiret
Globularia alypum L.
Halimium calycinum (L.) K. Koch
Halimium halimifolium (L.) Willk.
Helianthemum hirtum (L.) Mill.
Helianthemum marifolium (L.) Mill.
Helianthemum syriacum (Jacq.) Dum.-Courset
Helichrysum italicum (Roth) G. Don fil.
Helichrysum picardii Boiss. & Reuter
Helichrysum stoechas (L.) Moench
Jasminum fruticans L.
Juniperus oxycedrus L.
Juniperus phoenicea L.
Lavandula stoechas L.
Linum suffruticosum L.
Lonicera implexa Aiton
Lonicera periclymenum L.
Micromeria graeca (L.)
Bentham ex Reichenb.
Myrtus communis L.
Nerium oleander L.
Olea europaea L.
1
4
77
109
12
1
1
1
63
47
18
6
10
140
3
23
3
14
S
S
S
S
S
S
S
L
L
L
L
L
L
S
S
L
L
S
Antsa
Gómez and Oliveras (2003)
Short-distancea
Short-distancea
Unassiteda
Unassiteda
Unassiteda
Winda
Winda
Winda
Animala
Animal
Animala
Unassisted, Animal
Thanos et al. (1992)
Thanos et al. (1992)
Flynn et al. (2006)
Flynn et al. (2006)
Flynn et al. (2006)
Flynn et al. (2006)
Flynn et al. (2006)
Flynn et al. (2006)
Flynn et al. (2006)
Flynn et al. (2006)
Flynn et al. (2006)
Sánchez and Peco (2002)
Animal
Animala
Flynn et al. (2006)
Flynn et al. (2006)
100
4
144
L
L
L
Animal
Water
Animal
Osyris alba L.
Osyris lanceolata Hochst. & Steudel
Phagnalon rupestre (L.) DC.
Phagnalon saxatile (L.) Cass.
Phillyrea angustifolia L.
Phillyrea latifolia L.
Phlomis lychnitis L.
Phlomis purpurea L.
Pinus halepensis Miller
Pinus pinaster Aiton
Pinus pinea L.
Pistacia lentiscus L.
Pistacia terebinthus L.
Populus alba L.
Populus nigra L.
Pterospartum tridentatum (L.) Willk.
Pyrus bourgaeana Decne
Quercus coccifera L.
Quercus faginea Lam.
Quercus ilex L.
Quercus suber L.
Retama monosperma (L.) Boiss.
Retama sphaerocarpa (L.) Boiss.
Rhamnus alaternus L.
Rhamnus lycioides L.
Rosa canina L.
Rosa sempervirens L.
Rosmarinus officinalis L.
Rubia agostinhoi Dansereau & P. Silva
Rubia peregrina L.
Rubus ulmifolius Schott
Ruscus aculeatus L.
Salix alba L.
Salix atrocinerea Brot.
Santolina pectinata Lag.
Satureja obovata Lag.
Scrophularia canina L.
Sideritis arborescens Salzm. ex Bentham
Smilax aspera L.
Spartium junceum L.
Staehelina dubia L.
32
15
8
32
70
3
3
73
18
24
144
165
3
6
1
3
20
117
12
64
111
4
53
49
66
6
4
80
1
45
44
30
1
6
1
2
12
7
75
2
6
L
L
L
L
L
L
L
S
L
L
S
L
L
L
L
S
L
L
L
L
L
L
S
L
L
L
L
S
L
L
L
S
L
L
S
S
S
S
L
S
L
Animala
Animal
Water, wind
Water, winda
Animal
Animal
Wind
Unassisted
Wind or animala
Wind or animala
Traveset et al. (2001)
Herrera (1991)
Alcántara et al. (2000) and Rey
and Alcántara (2000)
Flynn et al. (2006)
Flynn et al. (2006)
Flynn et al. (2006)
Flynn et al. (2006)
Flynn et al. (2006)
Flynn et al. (2006)
Aparicio (1997)
Aparicio (1997)
Nathan and Ne’eman (2004) and Flynn et al. (2006)
Flynn et al. (2006)
Animal
Animal
Winda
Wind
Verdú and Garcı́a-Fayos (2001)
Flynn et al. (2006)
Flynn et al. (2006)
Flynn et al. (2006)
Animala
Animala
Animala
Animal
Animal
Animal
Wind, local dispersal
Animal
Animala
Animal
Animala
Ants
Animala
Animal
Animal
Unassisted
Wind, water
Wind, watera
Flynn et al. (2006)
Gómez Reyes (2003) and Pons and Pausas (2007)
Gómez Reyes (2003) and Pons and Pausas (2007)
Gómez Reyes (2003) and Pons and Pausas (2007)
Pons and Pausas (2007)
Dellafiore et al. (2006)
Pugnaire et al. (2006)
Flynn et al. (2006)
Flynn et al. (2006)
Flynn et al. (2006)
Flynn et al. (2006)
Bouman and Meeuse (1992)
Flynn et al. (2006)
Flynn et al. (2006)
Flynn et al. (2006)
Martı́nez-Palle and Aronne (1999)
Flynn et al. (2006)
Flynn et al. (2006)
Windd or animala
Bouman and Meeuse (1992)
Animal
Flynn et al. (2006)
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Appendix A (Continued )
N
Stauracanthus boivinii (Webb.) Samp.
Stauracanthus genistoides (Brot.) Samp.
Tamarix africana Poiret
Tamus communis L.
Teline linifolia (L.) Webb
Teucrium capitatum L.
Teucrium fruticans L.
Teucrium haenseleri Boiss.
Teucrium pseudochamaepitys L.
Thymbra capitata (L.) Cav.
Thymelaea argentata (Lam.) Pau
Thymelaea hirsuta (L.) Endl.
Thymelaea pubescens (L.) Meissner
Thymelaea villosa (L.) Endl.
Thymus albicans Hoffmanns. & Link
Thymus mastichina (L.) L.
Thymus zygis Loefl. ex L.
Ulex argenteus Welw. ex Webb
Ulex australis Clemente
Ulex baeticus Boiss.
Ulex eriocladus C. Vicioso
Ulex minor Roth
Ulex parviflorus Pourret
Ulmus minor Miller
Viola arborescens L.
Vitis vinifera L.
a
b
c
d
10
44
2
26
5
29
45
3
10
27
1
6
1
2
9
74
3
8
73
6
18
13
25
3
1
5
Assigned
dispersal category
S
S
L
L
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
L
S
L
Type of dispersal
Reference
Winda
Animal
Flynn et al. (2006)
Flynn et al. (2006)
Windd
Windd
Windd
Windd
Windd
Antsa
Antsa
Antsa
Antsa
Windd
Windd
Windd
Ants
Ants
Ants
Ants
Ants
Ants
Winda
Antsa
Animal
Bouman and Meeuse (1992)
Bouman and Meeuse (1992)
Bouman and Meeuse (1992)
Bouman and Meeuse (1992)
Bouman and Meeuse (1992)
de la Bandera and Traveset (2005)
de la Bandera and Traveset (2005)
de la Bandera and Traveset (2005)
de la Bandera and Traveset (2005)
Bouman and Meeuse (1992)
Bouman and Meeuse (1992)
Bouman and Meeuse (1992)
López-Vila and Garcı́a-Fayos (2005)
López-Vila and Garcı́a-Fayos (2005)
López-Vila and Garcı́a-Fayos (2005)
López-Vila and Garcı́a-Fayos (2005)
López-Vila and Garcı́a-Fayos (2005)
López-Vila and Garcı́a-Fayos (2005)
Flynn et al. (2006)
Flynn et al. (2006)
Flynn et al. (2006)
Reported in related congeneric species.
Candidate for non-standard long-distance dispersal (Ramos et al., 2006).
Candidate for non-standard long-distance dispersal (Manzano et al., 2005).
Probably local (Casper, 1987).
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